Overview

Dataset statistics

Number of variables25
Number of observations3953
Missing cells1047
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory772.2 KiB
Average record size in memory200.0 B

Variable types

Categorical15
Numeric10

Alerts

Name has a high cardinality: 3682 distinct valuesHigh cardinality
Email ID has a high cardinality: 3373 distinct valuesHigh cardinality
Dt_Applied has a high cardinality: 3953 distinct valuesHigh cardinality
University has a high cardinality: 3140 distinct valuesHigh cardinality
Zip Code has a high cardinality: 615 distinct valuesHigh cardinality
Loan Amnt is highly overall correlated with Funded amnt inv and 3 other fieldsHigh correlation
Funded amnt inv is highly overall correlated with Loan Amnt and 2 other fieldsHigh correlation
Int Rate is highly overall correlated with TERM and 2 other fieldsHigh correlation
INSTALLMENT is highly overall correlated with Loan Amnt and 2 other fieldsHigh correlation
Total Paymnt is highly overall correlated with Loan Amnt and 2 other fieldsHigh correlation
TERM is highly overall correlated with Loan Amnt and 3 other fieldsHigh correlation
GRADE is highly overall correlated with Int Rate and 2 other fieldsHigh correlation
Sub Grade is highly overall correlated with Int Rate and 2 other fieldsHigh correlation
Pub Rec is highly imbalanced (87.2%)Imbalance
Name has 271 (6.9%) missing valuesMissing
Email ID has 580 (14.7%) missing valuesMissing
Gender has 78 (2.0%) missing valuesMissing
University has 118 (3.0%) missing valuesMissing
Name is uniformly distributedUniform
Email ID is uniformly distributedUniform
Dt_Applied is uniformly distributedUniform
University is uniformly distributedUniform
Dt_Applied has unique valuesUnique
Delinq 2Yrs has 3628 (91.8%) zerosZeros
Inq Last 6Mths has 1822 (46.1%) zerosZeros
Revol Bal has 42 (1.1%) zerosZeros

Reproduction

Analysis started2023-02-04 17:17:35.181190
Analysis finished2023-02-04 17:17:52.695235
Duration17.51 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

Name
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct3682
Distinct (%)100.0%
Missing271
Missing (%)6.9%
Memory size31.0 KiB
Calley Giron
 
1
Glynnis Grinyakin
 
1
Taddeusz Gingold
 
1
Maighdiln Terrelly
 
1
Stacee Youtead
 
1
Other values (3677)
3677 

Length

Max length23
Median length20
Mean length14.032863
Min length7

Characters and Unicode

Total characters51669
Distinct characters58
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3682 ?
Unique (%)100.0%

Sample

1st rowCalley Giron
2nd rowLinus Stud
3rd rowLorelle Ambage
4th rowAnna-diane Larrat
5th rowGill Ruske

Common Values

ValueCountFrequency (%)
Calley Giron 1
 
< 0.1%
Glynnis Grinyakin 1
 
< 0.1%
Taddeusz Gingold 1
 
< 0.1%
Maighdiln Terrelly 1
 
< 0.1%
Stacee Youtead 1
 
< 0.1%
Hanson McGuire 1
 
< 0.1%
Tucky Harty 1
 
< 0.1%
Roby Phuprate 1
 
< 0.1%
Shel Graver 1
 
< 0.1%
Janelle Curtain 1
 
< 0.1%
Other values (3672) 3672
92.9%
(Missing) 271
 
6.9%

Length

2023-02-04T22:47:52.790979image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 20
 
0.3%
le 6
 
0.1%
van 5
 
0.1%
dee 5
 
0.1%
imogen 4
 
0.1%
lianna 4
 
0.1%
nichols 4
 
0.1%
harley 4
 
0.1%
salomo 4
 
0.1%
camel 4
 
0.1%
Other values (6460) 7355
99.2%

Most occurring characters

ValueCountFrequency (%)
e 5280
 
10.2%
a 4505
 
8.7%
3733
 
7.2%
n 3525
 
6.8%
i 3500
 
6.8%
r 3444
 
6.7%
l 3183
 
6.2%
o 2704
 
5.2%
t 2023
 
3.9%
s 1723
 
3.3%
Other values (48) 18049
34.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 40336
78.1%
Uppercase Letter 7550
 
14.6%
Space Separator 3733
 
7.2%
Other Punctuation 35
 
0.1%
Dash Punctuation 14
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5280
13.1%
a 4505
11.2%
n 3525
 
8.7%
i 3500
 
8.7%
r 3444
 
8.5%
l 3183
 
7.9%
o 2704
 
6.7%
t 2023
 
5.0%
s 1723
 
4.3%
d 1395
 
3.5%
Other values (16) 9054
22.4%
Uppercase Letter
ValueCountFrequency (%)
C 678
 
9.0%
M 600
 
7.9%
B 596
 
7.9%
S 573
 
7.6%
D 479
 
6.3%
A 451
 
6.0%
G 443
 
5.9%
R 400
 
5.3%
L 395
 
5.2%
H 326
 
4.3%
Other values (16) 2609
34.6%
Other Punctuation
ValueCountFrequency (%)
' 32
91.4%
. 2
 
5.7%
; 1
 
2.9%
Space Separator
ValueCountFrequency (%)
3733
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Close Punctuation
ValueCountFrequency (%)
] 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 47886
92.7%
Common 3783
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5280
 
11.0%
a 4505
 
9.4%
n 3525
 
7.4%
i 3500
 
7.3%
r 3444
 
7.2%
l 3183
 
6.6%
o 2704
 
5.6%
t 2023
 
4.2%
s 1723
 
3.6%
d 1395
 
2.9%
Other values (42) 16604
34.7%
Common
ValueCountFrequency (%)
3733
98.7%
' 32
 
0.8%
- 14
 
0.4%
. 2
 
0.1%
; 1
 
< 0.1%
] 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5280
 
10.2%
a 4505
 
8.7%
3733
 
7.2%
n 3525
 
6.8%
i 3500
 
6.8%
r 3444
 
6.7%
l 3183
 
6.2%
o 2704
 
5.2%
t 2023
 
3.9%
s 1723
 
3.3%
Other values (48) 18049
34.9%

Email ID
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct3373
Distinct (%)100.0%
Missing580
Missing (%)14.7%
Memory size31.0 KiB
cgiron0@ehow.com
 
1
ptinerhr@csmonitor.com
 
1
lnickolshg@guardian.co.uk
 
1
egrestehh@liveinternet.ru
 
1
bfarebrotherhi@sun.com
 
1
Other values (3368)
3368 

Length

Max length35
Median length31
Mean length21.833086
Min length11

Characters and Unicode

Total characters73643
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3373 ?
Unique (%)100.0%

Sample

1st rowcgiron0@ehow.com
2nd rowlstud1@washington.edu
3rd rowlambage2@wix.com
4th rowalarrat3@economist.com
5th rowemacfaul5@theatlantic.com

Common Values

ValueCountFrequency (%)
cgiron0@ehow.com 1
 
< 0.1%
ptinerhr@csmonitor.com 1
 
< 0.1%
lnickolshg@guardian.co.uk 1
 
< 0.1%
egrestehh@liveinternet.ru 1
 
< 0.1%
bfarebrotherhi@sun.com 1
 
< 0.1%
fesphj@google.fr 1
 
< 0.1%
rstrathearnhk@yahoo.co.jp 1
 
< 0.1%
ahanlonhl@hexun.com 1
 
< 0.1%
ccarehm@wikia.com 1
 
< 0.1%
hsuthernshn@sbwire.com 1
 
< 0.1%
Other values (3363) 3363
85.1%
(Missing) 580
 
14.7%

Length

2023-02-04T22:47:52.945733image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cgiron0@ehow.com 1
 
< 0.1%
jfleetwood1m@google.com 1
 
< 0.1%
aalens@slashdot.org 1
 
< 0.1%
swoollacottd@geocities.com 1
 
< 0.1%
lambage2@wix.com 1
 
< 0.1%
alarrat3@economist.com 1
 
< 0.1%
emacfaul5@theatlantic.com 1
 
< 0.1%
arainard6@virginia.edu 1
 
< 0.1%
ehamby7@prnewswire.com 1
 
< 0.1%
stoomer8@home.pl 1
 
< 0.1%
Other values (3363) 3363
99.7%

Most occurring characters

ValueCountFrequency (%)
o 6259
 
8.5%
e 5765
 
7.8%
c 4676
 
6.3%
a 4491
 
6.1%
m 4076
 
5.5%
. 3699
 
5.0%
r 3657
 
5.0%
n 3612
 
4.9%
i 3589
 
4.9%
@ 3373
 
4.6%
Other values (29) 30446
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 64215
87.2%
Other Punctuation 7072
 
9.6%
Decimal Number 2273
 
3.1%
Dash Punctuation 83
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 6259
 
9.7%
e 5765
 
9.0%
c 4676
 
7.3%
a 4491
 
7.0%
m 4076
 
6.3%
r 3657
 
5.7%
n 3612
 
5.6%
i 3589
 
5.6%
l 3340
 
5.2%
s 3154
 
4.9%
Other values (16) 21596
33.6%
Decimal Number
ValueCountFrequency (%)
2 265
11.7%
3 260
11.4%
1 260
11.4%
4 243
10.7%
6 243
10.7%
8 239
10.5%
5 227
10.0%
9 215
9.5%
7 210
9.2%
0 111
4.9%
Other Punctuation
ValueCountFrequency (%)
. 3699
52.3%
@ 3373
47.7%
Dash Punctuation
ValueCountFrequency (%)
- 83
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 64215
87.2%
Common 9428
 
12.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 6259
 
9.7%
e 5765
 
9.0%
c 4676
 
7.3%
a 4491
 
7.0%
m 4076
 
6.3%
r 3657
 
5.7%
n 3612
 
5.6%
i 3589
 
5.6%
l 3340
 
5.2%
s 3154
 
4.9%
Other values (16) 21596
33.6%
Common
ValueCountFrequency (%)
. 3699
39.2%
@ 3373
35.8%
2 265
 
2.8%
3 260
 
2.8%
1 260
 
2.8%
4 243
 
2.6%
6 243
 
2.6%
8 239
 
2.5%
5 227
 
2.4%
9 215
 
2.3%
Other values (3) 404
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73643
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 6259
 
8.5%
e 5765
 
7.8%
c 4676
 
6.3%
a 4491
 
6.1%
m 4076
 
5.5%
. 3699
 
5.0%
r 3657
 
5.0%
n 3612
 
4.9%
i 3589
 
4.9%
@ 3373
 
4.6%
Other values (29) 30446
41.3%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing78
Missing (%)2.0%
Memory size31.0 KiB
Male
1970 
Female
1905 

Length

Max length6
Median length4
Mean length4.9832258
Min length4

Characters and Unicode

Total characters19310
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 1970
49.8%
Female 1905
48.2%
(Missing) 78
 
2.0%

Length

2023-02-04T22:47:53.091343image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-04T22:47:53.227461image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
male 1970
50.8%
female 1905
49.2%

Most occurring characters

ValueCountFrequency (%)
e 5780
29.9%
a 3875
20.1%
l 3875
20.1%
M 1970
 
10.2%
F 1905
 
9.9%
m 1905
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15435
79.9%
Uppercase Letter 3875
 
20.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5780
37.4%
a 3875
25.1%
l 3875
25.1%
m 1905
 
12.3%
Uppercase Letter
ValueCountFrequency (%)
M 1970
50.8%
F 1905
49.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 19310
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5780
29.9%
a 3875
20.1%
l 3875
20.1%
M 1970
 
10.2%
F 1905
 
9.9%
m 1905
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5780
29.9%
a 3875
20.1%
l 3875
20.1%
M 1970
 
10.2%
F 1905
 
9.9%
m 1905
 
9.9%

Dt_Applied
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct3953
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
01/01/81
 
1
12/03/88
 
1
14/03/88
 
1
15/03/88
 
1
16/03/88
 
1
Other values (3948)
3948 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters31624
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3953 ?
Unique (%)100.0%

Sample

1st row01/01/81
2nd row02/01/81
3rd row03/01/81
4th row04/01/81
5th row05/01/81

Common Values

ValueCountFrequency (%)
01/01/81 1
 
< 0.1%
12/03/88 1
 
< 0.1%
14/03/88 1
 
< 0.1%
15/03/88 1
 
< 0.1%
16/03/88 1
 
< 0.1%
17/03/88 1
 
< 0.1%
18/03/88 1
 
< 0.1%
19/03/88 1
 
< 0.1%
20/03/88 1
 
< 0.1%
21/03/88 1
 
< 0.1%
Other values (3943) 3943
99.7%

Length

2023-02-04T22:47:53.329381image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01/01/81 1
 
< 0.1%
31/01/81 1
 
< 0.1%
30/01/81 1
 
< 0.1%
03/01/81 1
 
< 0.1%
04/01/81 1
 
< 0.1%
05/01/81 1
 
< 0.1%
06/01/81 1
 
< 0.1%
07/01/81 1
 
< 0.1%
08/01/81 1
 
< 0.1%
09/01/81 1
 
< 0.1%
Other values (3943) 3943
99.7%

Most occurring characters

ValueCountFrequency (%)
/ 7906
25.0%
0 5259
16.6%
8 4381
13.9%
1 4020
12.7%
2 2664
 
8.4%
9 1744
 
5.5%
3 1288
 
4.1%
5 1096
 
3.5%
7 1096
 
3.5%
4 1085
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23718
75.0%
Other Punctuation 7906
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5259
22.2%
8 4381
18.5%
1 4020
16.9%
2 2664
11.2%
9 1744
 
7.4%
3 1288
 
5.4%
5 1096
 
4.6%
7 1096
 
4.6%
4 1085
 
4.6%
6 1085
 
4.6%
Other Punctuation
ValueCountFrequency (%)
/ 7906
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 7906
25.0%
0 5259
16.6%
8 4381
13.9%
1 4020
12.7%
2 2664
 
8.4%
9 1744
 
5.5%
3 1288
 
4.1%
5 1096
 
3.5%
7 1096
 
3.5%
4 1085
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 7906
25.0%
0 5259
16.6%
8 4381
13.9%
1 4020
12.7%
2 2664
 
8.4%
9 1744
 
5.5%
3 1288
 
4.1%
5 1096
 
3.5%
7 1096
 
3.5%
4 1085
 
3.4%

University
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct3140
Distinct (%)81.9%
Missing118
Missing (%)3.0%
Memory size31.0 KiB
Abant Izzet Baysal University
 
4
Jiangxi University of Traditional Chinese Medicine
 
4
Fukuoka Institute of Technology
 
4
Tampere Polytechnic
 
4
Phillips Graduate Institute
 
4
Other values (3135)
3815 

Length

Max length114
Median length72
Mean length30.491525
Min length11

Characters and Unicode

Total characters116935
Distinct characters98
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2542 ?
Unique (%)66.3%

Sample

1st rowWarner Southern College
2nd rowShri Lal Bahadur Shastri Rashtriya Sanskrit Vidyapeetha
3rd rowTechnische Universität Bergakademie Freiberg
4th rowDivine Word College of Legazpi
5th rowEast China Jiao Tong University

Common Values

ValueCountFrequency (%)
Abant Izzet Baysal University 4
 
0.1%
Jiangxi University of Traditional Chinese Medicine 4
 
0.1%
Fukuoka Institute of Technology 4
 
0.1%
Tampere Polytechnic 4
 
0.1%
Phillips Graduate Institute 4
 
0.1%
Stavropol State Technical University 4
 
0.1%
Christchurch Polytechnic Institute of Technology 4
 
0.1%
Universidad Tecnológica de México 4
 
0.1%
Carlow College 4
 
0.1%
Universidad de Congreso 4
 
0.1%
Other values (3130) 3795
96.0%
(Missing) 118
 
3.0%

Length

2023-02-04T22:47:53.475463image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
university 2142
 
14.3%
of 1144
 
7.6%
college 544
 
3.6%
de 397
 
2.7%
universidad 307
 
2.0%
state 274
 
1.8%
institute 220
 
1.5%
and 197
 
1.3%
technology 195
 
1.3%
113
 
0.8%
Other values (3562) 9447
63.1%

Most occurring characters

ValueCountFrequency (%)
11198
 
9.6%
i 10758
 
9.2%
e 10464
 
8.9%
n 8336
 
7.1%
a 7981
 
6.8%
t 6906
 
5.9%
r 6300
 
5.4%
o 6107
 
5.2%
s 5789
 
5.0%
l 4376
 
3.7%
Other values (88) 38720
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 91706
78.4%
Uppercase Letter 13156
 
11.3%
Space Separator 11198
 
9.6%
Other Punctuation 508
 
0.4%
Dash Punctuation 186
 
0.2%
Open Punctuation 79
 
0.1%
Close Punctuation 79
 
0.1%
Decimal Number 19
 
< 0.1%
Control 2
 
< 0.1%
Initial Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 10758
11.7%
e 10464
11.4%
n 8336
 
9.1%
a 7981
 
8.7%
t 6906
 
7.5%
r 6300
 
6.9%
o 6107
 
6.7%
s 5789
 
6.3%
l 4376
 
4.8%
y 3155
 
3.4%
Other values (37) 21534
23.5%
Uppercase Letter
ValueCountFrequency (%)
U 2851
21.7%
S 1352
10.3%
C 1236
 
9.4%
A 857
 
6.5%
M 785
 
6.0%
T 762
 
5.8%
I 708
 
5.4%
N 522
 
4.0%
P 492
 
3.7%
B 422
 
3.2%
Other values (19) 3169
24.1%
Decimal Number
ValueCountFrequency (%)
1 7
36.8%
7 3
15.8%
4 2
 
10.5%
5 2
 
10.5%
9 2
 
10.5%
8 1
 
5.3%
3 1
 
5.3%
2 1
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 200
39.4%
. 106
20.9%
' 91
17.9%
" 62
 
12.2%
& 35
 
6.9%
/ 14
 
2.8%
Control
ValueCountFrequency (%)
“ 1
50.0%
” 1
50.0%
Space Separator
ValueCountFrequency (%)
11198
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 186
100.0%
Open Punctuation
ValueCountFrequency (%)
( 79
100.0%
Close Punctuation
ValueCountFrequency (%)
) 79
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 104862
89.7%
Common 12073
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 10758
 
10.3%
e 10464
 
10.0%
n 8336
 
7.9%
a 7981
 
7.6%
t 6906
 
6.6%
r 6300
 
6.0%
o 6107
 
5.8%
s 5789
 
5.5%
l 4376
 
4.2%
y 3155
 
3.0%
Other values (66) 34690
33.1%
Common
ValueCountFrequency (%)
11198
92.8%
, 200
 
1.7%
- 186
 
1.5%
. 106
 
0.9%
' 91
 
0.8%
( 79
 
0.7%
) 79
 
0.7%
" 62
 
0.5%
& 35
 
0.3%
/ 14
 
0.1%
Other values (12) 23
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 116357
99.5%
None 576
 
0.5%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11198
 
9.6%
i 10758
 
9.2%
e 10464
 
9.0%
n 8336
 
7.2%
a 7981
 
6.9%
t 6906
 
5.9%
r 6300
 
5.4%
o 6107
 
5.2%
s 5789
 
5.0%
l 4376
 
3.8%
Other values (60) 38142
32.8%
None
ValueCountFrequency (%)
é 211
36.6%
ó 90
15.6%
ä 65
 
11.3%
ü 59
 
10.2%
á 43
 
7.5%
í 33
 
5.7%
è 11
 
1.9%
ñ 9
 
1.6%
ç 9
 
1.6%
ã 8
 
1.4%
Other values (16) 38
 
6.6%
Punctuation
ValueCountFrequency (%)
1
50.0%
1
50.0%

Loan Amnt
Real number (ℝ)

Distinct434
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13017.499
Minimum1000
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-02-04T22:47:53.634511image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile3000
Q16500
median12000
Q317625
95-th percentile30000
Maximum35000
Range34000
Interquartile range (IQR)11125

Descriptive statistics

Standard deviation8155.3303
Coefficient of variation (CV)0.62648978
Kurtosis0.32585321
Mean13017.499
Median Absolute Deviation (MAD)5500
Skewness0.92331288
Sum51458175
Variance66509413
MonotonicityNot monotonic
2023-02-04T22:47:53.788408image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12000 315
 
8.0%
10000 259
 
6.6%
15000 190
 
4.8%
20000 174
 
4.4%
6000 165
 
4.2%
5000 153
 
3.9%
35000 143
 
3.6%
8000 124
 
3.1%
16000 99
 
2.5%
25000 97
 
2.5%
Other values (424) 2234
56.5%
ValueCountFrequency (%)
1000 21
0.5%
1100 1
 
< 0.1%
1200 9
0.2%
1300 2
 
0.1%
1325 1
 
< 0.1%
1400 3
 
0.1%
1450 2
 
0.1%
1500 11
0.3%
1600 6
 
0.2%
1700 1
 
< 0.1%
ValueCountFrequency (%)
35000 143
3.6%
34475 1
 
< 0.1%
34000 2
 
0.1%
33950 1
 
< 0.1%
33600 2
 
0.1%
33425 1
 
< 0.1%
33000 1
 
< 0.1%
32875 1
 
< 0.1%
32275 1
 
< 0.1%
32000 3
 
0.1%

Funded amnt inv
Real number (ℝ)

Distinct828
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12809.792
Minimum750
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-02-04T22:47:53.950501image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum750
5-th percentile3000
Q16500
median11775
Q317000
95-th percentile29735
Maximum35000
Range34250
Interquartile range (IQR)10500

Descriptive statistics

Standard deviation7935.9077
Coefficient of variation (CV)0.61951885
Kurtosis0.39513707
Mean12809.792
Median Absolute Deviation (MAD)5275
Skewness0.92631719
Sum50637108
Variance62978631
MonotonicityNot monotonic
2023-02-04T22:47:54.409983image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12000 249
 
6.3%
10000 222
 
5.6%
6000 153
 
3.9%
5000 143
 
3.6%
15000 139
 
3.5%
8000 113
 
2.9%
7000 87
 
2.2%
3000 74
 
1.9%
20000 72
 
1.8%
14000 64
 
1.6%
Other values (818) 2637
66.7%
ValueCountFrequency (%)
750 1
 
< 0.1%
1000 20
0.5%
1100 1
 
< 0.1%
1200 9
0.2%
1300 2
 
0.1%
1325 1
 
< 0.1%
1400 3
 
0.1%
1450 2
 
0.1%
1500 11
0.3%
1600 6
 
0.2%
ValueCountFrequency (%)
35000 37
0.9%
34997.35245 1
 
< 0.1%
34993.65539 1
 
< 0.1%
34987.98452 1
 
< 0.1%
34987.27101 1
 
< 0.1%
34977.34674 1
 
< 0.1%
34975.81636 1
 
< 0.1%
34975 14
 
0.4%
34972.8295 1
 
< 0.1%
34972.50393 1
 
< 0.1%

TERM
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
36 months
2687 
60 months
1266 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters39530
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 36 months
2nd row 60 months
3rd row 36 months
4th row 36 months
5th row 60 months

Common Values

ValueCountFrequency (%)
36 months 2687
68.0%
60 months 1266
32.0%

Length

2023-02-04T22:47:54.548618image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-04T22:47:54.673824image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
months 3953
50.0%
36 2687
34.0%
60 1266
 
16.0%

Most occurring characters

ValueCountFrequency (%)
7906
20.0%
6 3953
10.0%
m 3953
10.0%
o 3953
10.0%
n 3953
10.0%
t 3953
10.0%
h 3953
10.0%
s 3953
10.0%
3 2687
 
6.8%
0 1266
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23718
60.0%
Space Separator 7906
 
20.0%
Decimal Number 7906
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 3953
16.7%
o 3953
16.7%
n 3953
16.7%
t 3953
16.7%
h 3953
16.7%
s 3953
16.7%
Decimal Number
ValueCountFrequency (%)
6 3953
50.0%
3 2687
34.0%
0 1266
 
16.0%
Space Separator
ValueCountFrequency (%)
7906
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23718
60.0%
Common 15812
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 3953
16.7%
o 3953
16.7%
n 3953
16.7%
t 3953
16.7%
h 3953
16.7%
s 3953
16.7%
Common
ValueCountFrequency (%)
7906
50.0%
6 3953
25.0%
3 2687
 
17.0%
0 1266
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39530
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7906
20.0%
6 3953
10.0%
m 3953
10.0%
o 3953
10.0%
n 3953
10.0%
t 3953
10.0%
h 3953
10.0%
s 3953
10.0%
3 2687
 
6.8%
0 1266
 
3.2%

Int Rate
Real number (ℝ)

Distinct35
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12969087
Minimum0.06
Maximum0.241
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-02-04T22:47:54.792969image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile0.066
Q10.099
median0.127
Q30.16
95-th percentile0.203
Maximum0.241
Range0.181
Interquartile range (IQR)0.061

Descriptive statistics

Standard deviation0.041609315
Coefficient of variation (CV)0.32083458
Kurtosis-0.69519246
Mean0.12969087
Median Absolute Deviation (MAD)0.033
Skewness0.22641622
Sum512.668
Variance0.0017313351
MonotonicityNot monotonic
2023-02-04T22:47:54.930752image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.117 324
 
8.2%
0.127 259
 
6.6%
0.079 259
 
6.6%
0.124 254
 
6.4%
0.135 231
 
5.8%
0.143 226
 
5.7%
0.107 213
 
5.4%
0.099 211
 
5.3%
0.089 198
 
5.0%
0.06 160
 
4.0%
Other values (25) 1618
40.9%
ValueCountFrequency (%)
0.06 160
4.0%
0.066 156
3.9%
0.075 137
3.5%
0.079 259
6.6%
0.089 198
5.0%
0.099 211
5.3%
0.107 213
5.4%
0.117 324
8.2%
0.124 254
6.4%
0.127 259
6.6%
ValueCountFrequency (%)
0.241 2
 
0.1%
0.239 6
 
0.2%
0.235 6
 
0.2%
0.231 4
 
0.1%
0.227 6
 
0.2%
0.224 15
 
0.4%
0.221 19
0.5%
0.217 24
0.6%
0.213 28
0.7%
0.209 39
1.0%

INSTALLMENT
Real number (ℝ)

Distinct1923
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean375.20734
Minimum32.23
Maximum1283.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-02-04T22:47:55.086595image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum32.23
5-th percentile93.88
Q1205.86
median336
Q3494.59
95-th percentile813.626
Maximum1283.5
Range1251.27
Interquartile range (IQR)288.73

Descriptive statistics

Standard deviation220.26115
Coefficient of variation (CV)0.5870385
Kurtosis0.89008542
Mean375.20734
Median Absolute Deviation (MAD)140.06
Skewness0.98371682
Sum1483194.6
Variance48514.975
MonotonicityNot monotonic
2023-02-04T22:47:55.247158image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
330.76 27
 
0.7%
396.92 25
 
0.6%
325.74 22
 
0.6%
386.7 21
 
0.5%
339.31 20
 
0.5%
334.16 19
 
0.5%
322.25 19
 
0.5%
343.09 18
 
0.5%
190.52 18
 
0.5%
368.45 17
 
0.4%
Other values (1913) 3747
94.8%
ValueCountFrequency (%)
32.23 1
 
< 0.1%
32.58 2
0.1%
33.08 2
0.1%
33.55 1
 
< 0.1%
33.94 3
0.1%
34.31 1
 
< 0.1%
34.5 3
0.1%
34.8 2
0.1%
35.14 1
 
< 0.1%
35.31 4
0.1%
ValueCountFrequency (%)
1283.5 1
 
< 0.1%
1276.6 1
 
< 0.1%
1269.73 1
 
< 0.1%
1243.85 1
 
< 0.1%
1222.03 1
 
< 0.1%
1203.66 1
 
< 0.1%
1200.82 2
 
0.1%
1157.66 1
 
< 0.1%
1142.94 1
 
< 0.1%
1140.07 5
0.1%

GRADE
Categorical

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
B
1262 
A
908 
C
811 
D
510 
E
313 
Other values (2)
149 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3953
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowC
3rd rowC
4th rowC
5th rowB

Common Values

ValueCountFrequency (%)
B 1262
31.9%
A 908
23.0%
C 811
20.5%
D 510
12.9%
E 313
 
7.9%
F 125
 
3.2%
G 24
 
0.6%

Length

2023-02-04T22:47:55.379253image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-04T22:47:55.520140image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
b 1262
31.9%
a 908
23.0%
c 811
20.5%
d 510
12.9%
e 313
 
7.9%
f 125
 
3.2%
g 24
 
0.6%

Most occurring characters

ValueCountFrequency (%)
B 1262
31.9%
A 908
23.0%
C 811
20.5%
D 510
12.9%
E 313
 
7.9%
F 125
 
3.2%
G 24
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3953
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 1262
31.9%
A 908
23.0%
C 811
20.5%
D 510
12.9%
E 313
 
7.9%
F 125
 
3.2%
G 24
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 3953
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 1262
31.9%
A 908
23.0%
C 811
20.5%
D 510
12.9%
E 313
 
7.9%
F 125
 
3.2%
G 24
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3953
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 1262
31.9%
A 908
23.0%
C 811
20.5%
D 510
12.9%
E 313
 
7.9%
F 125
 
3.2%
G 24
 
0.6%

Sub Grade
Categorical

Distinct35
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
B3
324 
B5
 
260
A4
 
259
B4
 
254
C1
 
231
Other values (30)
2625 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters7906
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB2
2nd rowC4
3rd rowC5
4th rowC1
5th rowB5

Common Values

ValueCountFrequency (%)
B3 324
 
8.2%
B5 260
 
6.6%
A4 259
 
6.6%
B4 254
 
6.4%
C1 231
 
5.8%
C2 227
 
5.7%
B2 213
 
5.4%
B1 211
 
5.3%
A5 198
 
5.0%
A1 158
 
4.0%
Other values (25) 1618
40.9%

Length

2023-02-04T22:47:55.647518image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b3 324
 
8.2%
b5 260
 
6.6%
a4 259
 
6.6%
b4 254
 
6.4%
c1 231
 
5.8%
c2 227
 
5.7%
b2 213
 
5.4%
b1 211
 
5.3%
a5 198
 
5.0%
a1 158
 
4.0%
Other values (25) 1618
40.9%

Most occurring characters

ValueCountFrequency (%)
B 1262
16.0%
A 908
11.5%
2 832
10.5%
C 811
10.3%
4 806
10.2%
3 803
10.2%
1 797
10.1%
5 715
9.0%
D 510
6.5%
E 313
 
4.0%
Other values (2) 149
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3953
50.0%
Decimal Number 3953
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 1262
31.9%
A 908
23.0%
C 811
20.5%
D 510
12.9%
E 313
 
7.9%
F 125
 
3.2%
G 24
 
0.6%
Decimal Number
ValueCountFrequency (%)
2 832
21.0%
4 806
20.4%
3 803
20.3%
1 797
20.2%
5 715
18.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 3953
50.0%
Common 3953
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 1262
31.9%
A 908
23.0%
C 811
20.5%
D 510
12.9%
E 313
 
7.9%
F 125
 
3.2%
G 24
 
0.6%
Common
ValueCountFrequency (%)
2 832
21.0%
4 806
20.4%
3 803
20.3%
1 797
20.2%
5 715
18.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 1262
16.0%
A 908
11.5%
2 832
10.5%
C 811
10.3%
4 806
10.2%
3 803
10.2%
1 797
10.1%
5 715
9.0%
D 510
6.5%
E 313
 
4.0%
Other values (2) 149
 
1.9%

Home Ownership
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
RENT
2081 
MORTGAGE
1577 
OWN
295 

Length

Max length8
Median length4
Mean length5.5211232
Min length3

Characters and Unicode

Total characters21825
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowRENT
3rd rowRENT
4th rowRENT
5th rowRENT

Common Values

ValueCountFrequency (%)
RENT 2081
52.6%
MORTGAGE 1577
39.9%
OWN 295
 
7.5%

Length

2023-02-04T22:47:55.759531image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-04T22:47:55.886230image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
rent 2081
52.6%
mortgage 1577
39.9%
own 295
 
7.5%

Most occurring characters

ValueCountFrequency (%)
R 3658
16.8%
E 3658
16.8%
T 3658
16.8%
G 3154
14.5%
N 2376
10.9%
O 1872
8.6%
M 1577
7.2%
A 1577
7.2%
W 295
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 21825
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 3658
16.8%
E 3658
16.8%
T 3658
16.8%
G 3154
14.5%
N 2376
10.9%
O 1872
8.6%
M 1577
7.2%
A 1577
7.2%
W 295
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 21825
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 3658
16.8%
E 3658
16.8%
T 3658
16.8%
G 3154
14.5%
N 2376
10.9%
O 1872
8.6%
M 1577
7.2%
A 1577
7.2%
W 295
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21825
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 3658
16.8%
E 3658
16.8%
T 3658
16.8%
G 3154
14.5%
N 2376
10.9%
O 1872
8.6%
M 1577
7.2%
A 1577
7.2%
W 295
 
1.4%

Annual Inc
Real number (ℝ)

Distinct813
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66175.974
Minimum8280
Maximum550000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-02-04T22:47:56.016880image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum8280
5-th percentile25000
Q140100
median57000
Q380000
95-th percentile135880
Maximum550000
Range541720
Interquartile range (IQR)39900

Descriptive statistics

Standard deviation40498.804
Coefficient of variation (CV)0.61198653
Kurtosis18.714261
Mean66175.974
Median Absolute Deviation (MAD)18000
Skewness3.0582009
Sum2.6159362 × 108
Variance1.6401531 × 109
MonotonicityNot monotonic
2023-02-04T22:47:56.178228image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 154
 
3.9%
50000 149
 
3.8%
40000 120
 
3.0%
75000 120
 
3.0%
45000 114
 
2.9%
70000 96
 
2.4%
80000 93
 
2.4%
30000 93
 
2.4%
65000 88
 
2.2%
35000 82
 
2.1%
Other values (803) 2844
71.9%
ValueCountFrequency (%)
8280 1
 
< 0.1%
8400 1
 
< 0.1%
9600 1
 
< 0.1%
9960 1
 
< 0.1%
10000 1
 
< 0.1%
11000 1
 
< 0.1%
11340 1
 
< 0.1%
11820 1
 
< 0.1%
12000 8
0.2%
12252 1
 
< 0.1%
ValueCountFrequency (%)
550000 1
 
< 0.1%
525000 1
 
< 0.1%
408000 1
 
< 0.1%
400000 2
 
0.1%
365000 1
 
< 0.1%
350000 1
 
< 0.1%
325000 1
 
< 0.1%
300000 5
0.1%
290000 1
 
< 0.1%
281000 1
 
< 0.1%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
Verified
1515 
Not Verified
1247 
Source Verified
1191 

Length

Max length15
Median length12
Mean length11.370858
Min length8

Characters and Unicode

Total characters44949
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVerified
2nd rowSource Verified
3rd rowNot Verified
4th rowSource Verified
5th rowSource Verified

Common Values

ValueCountFrequency (%)
Verified 1515
38.3%
Not Verified 1247
31.5%
Source Verified 1191
30.1%

Length

2023-02-04T22:47:56.318820image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-04T22:47:56.461473image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
verified 3953
61.9%
not 1247
 
19.5%
source 1191
 
18.6%

Most occurring characters

ValueCountFrequency (%)
e 9097
20.2%
i 7906
17.6%
r 5144
11.4%
V 3953
8.8%
f 3953
8.8%
d 3953
8.8%
o 2438
 
5.4%
2438
 
5.4%
N 1247
 
2.8%
t 1247
 
2.8%
Other values (3) 3573
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 36120
80.4%
Uppercase Letter 6391
 
14.2%
Space Separator 2438
 
5.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9097
25.2%
i 7906
21.9%
r 5144
14.2%
f 3953
10.9%
d 3953
10.9%
o 2438
 
6.7%
t 1247
 
3.5%
u 1191
 
3.3%
c 1191
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
V 3953
61.9%
N 1247
 
19.5%
S 1191
 
18.6%
Space Separator
ValueCountFrequency (%)
2438
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 42511
94.6%
Common 2438
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9097
21.4%
i 7906
18.6%
r 5144
12.1%
V 3953
9.3%
f 3953
9.3%
d 3953
9.3%
o 2438
 
5.7%
N 1247
 
2.9%
t 1247
 
2.9%
S 1191
 
2.8%
Other values (2) 2382
 
5.6%
Common
ValueCountFrequency (%)
2438
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9097
20.2%
i 7906
17.6%
r 5144
11.4%
V 3953
8.8%
f 3953
8.8%
d 3953
8.8%
o 2438
 
5.4%
2438
 
5.4%
N 1247
 
2.8%
t 1247
 
2.8%
Other values (3) 3573
 
7.9%

Loan Writeoff
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
0
3275 
1
678 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3953
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3275
82.8%
1 678
 
17.2%

Length

2023-02-04T22:47:56.572992image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-04T22:47:56.693703image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3275
82.8%
1 678
 
17.2%

Most occurring characters

ValueCountFrequency (%)
0 3275
82.8%
1 678
 
17.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3953
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3275
82.8%
1 678
 
17.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3953
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3275
82.8%
1 678
 
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3953
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3275
82.8%
1 678
 
17.2%

PURPOSE
Categorical

Distinct13
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
debt_consolidation
2102 
credit_card
792 
other
297 
home_improvement
 
196
small_business
 
145
Other values (8)
421 

Length

Max length18
Median length18
Mean length14.283076
Min length3

Characters and Unicode

Total characters56461
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit_card
2nd rowcar
3rd rowsmall_business
4th rowother
5th rowother

Common Values

ValueCountFrequency (%)
debt_consolidation 2102
53.2%
credit_card 792
 
20.0%
other 297
 
7.5%
home_improvement 196
 
5.0%
small_business 145
 
3.7%
major_purchase 100
 
2.5%
car 90
 
2.3%
wedding 63
 
1.6%
medical 52
 
1.3%
moving 39
 
1.0%
Other values (3) 77
 
1.9%

Length

2023-02-04T22:47:56.807346image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation 2102
53.2%
credit_card 792
 
20.0%
other 297
 
7.5%
home_improvement 196
 
5.0%
small_business 145
 
3.7%
major_purchase 100
 
2.5%
car 90
 
2.3%
wedding 63
 
1.6%
medical 52
 
1.3%
moving 39
 
1.0%
Other values (3) 77
 
1.9%

Most occurring characters

ValueCountFrequency (%)
o 7205
12.8%
d 5966
10.6%
i 5525
9.8%
t 5523
9.8%
n 4693
8.3%
e 4206
7.4%
c 3962
7.0%
a 3455
 
6.1%
_ 3341
 
5.9%
s 2819
 
5.0%
Other values (12) 9766
17.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53120
94.1%
Connector Punctuation 3341
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 7205
13.6%
d 5966
11.2%
i 5525
10.4%
t 5523
10.4%
n 4693
8.8%
e 4206
7.9%
c 3962
7.5%
a 3455
6.5%
s 2819
 
5.3%
l 2450
 
4.6%
Other values (11) 7316
13.8%
Connector Punctuation
ValueCountFrequency (%)
_ 3341
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 53120
94.1%
Common 3341
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 7205
13.6%
d 5966
11.2%
i 5525
10.4%
t 5523
10.4%
n 4693
8.8%
e 4206
7.9%
c 3962
7.5%
a 3455
6.5%
s 2819
 
5.3%
l 2450
 
4.6%
Other values (11) 7316
13.8%
Common
ValueCountFrequency (%)
_ 3341
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56461
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 7205
12.8%
d 5966
10.6%
i 5525
9.8%
t 5523
9.8%
n 4693
8.3%
e 4206
7.4%
c 3962
7.0%
a 3455
 
6.1%
_ 3341
 
5.9%
s 2819
 
5.0%
Other values (12) 9766
17.3%

Zip Code
Categorical

Distinct615
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
606xx
 
55
900xx
 
55
100xx
 
54
112xx
 
50
945xx
 
49
Other values (610)
3690 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters19765
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)3.7%

Sample

1st row860xx
2nd row309xx
3rd row606xx
4th row917xx
5th row972xx

Common Values

ValueCountFrequency (%)
606xx 55
 
1.4%
900xx 55
 
1.4%
100xx 54
 
1.4%
112xx 50
 
1.3%
945xx 49
 
1.2%
070xx 45
 
1.1%
331xx 44
 
1.1%
750xx 41
 
1.0%
300xx 41
 
1.0%
113xx 40
 
1.0%
Other values (605) 3479
88.0%

Length

2023-02-04T22:47:56.943597image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
606xx 55
 
1.4%
900xx 55
 
1.4%
100xx 54
 
1.4%
112xx 50
 
1.3%
945xx 49
 
1.2%
070xx 45
 
1.1%
331xx 44
 
1.1%
750xx 41
 
1.0%
300xx 41
 
1.0%
113xx 40
 
1.0%
Other values (605) 3479
88.0%

Most occurring characters

ValueCountFrequency (%)
x 7906
40.0%
0 1903
 
9.6%
1 1535
 
7.8%
9 1309
 
6.6%
2 1309
 
6.6%
3 1269
 
6.4%
7 1023
 
5.2%
5 924
 
4.7%
4 914
 
4.6%
8 855
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11859
60.0%
Lowercase Letter 7906
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1903
16.0%
1 1535
12.9%
9 1309
11.0%
2 1309
11.0%
3 1269
10.7%
7 1023
8.6%
5 924
7.8%
4 914
7.7%
8 855
7.2%
6 818
6.9%
Lowercase Letter
ValueCountFrequency (%)
x 7906
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11859
60.0%
Latin 7906
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1903
16.0%
1 1535
12.9%
9 1309
11.0%
2 1309
11.0%
3 1269
10.7%
7 1023
8.6%
5 924
7.8%
4 914
7.7%
8 855
7.2%
6 818
6.9%
Latin
ValueCountFrequency (%)
x 7906
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19765
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
x 7906
40.0%
0 1903
 
9.6%
1 1535
 
7.8%
9 1309
 
6.6%
2 1309
 
6.6%
3 1269
 
6.4%
7 1023
 
5.2%
5 924
 
4.7%
4 914
 
4.6%
8 855
 
4.3%

Add State
Categorical

Distinct43
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
CA
729 
NY
372 
FL
304 
TX
273 
NJ
 
181
Other values (38)
2094 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters7906
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAZ
2nd rowGA
3rd rowIL
4th rowCA
5th rowOR

Common Values

ValueCountFrequency (%)
CA 729
18.4%
NY 372
 
9.4%
FL 304
 
7.7%
TX 273
 
6.9%
NJ 181
 
4.6%
IL 155
 
3.9%
GA 146
 
3.7%
PA 136
 
3.4%
VA 130
 
3.3%
OH 124
 
3.1%
Other values (33) 1403
35.5%

Length

2023-02-04T22:47:57.062787image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 729
18.4%
ny 372
 
9.4%
fl 304
 
7.7%
tx 273
 
6.9%
nj 181
 
4.6%
il 155
 
3.9%
ga 146
 
3.7%
pa 136
 
3.4%
va 130
 
3.3%
oh 124
 
3.1%
Other values (33) 1403
35.5%

Most occurring characters

ValueCountFrequency (%)
A 1557
19.7%
C 1032
13.1%
N 803
10.2%
L 537
 
6.8%
M 413
 
5.2%
Y 407
 
5.1%
T 394
 
5.0%
O 353
 
4.5%
I 309
 
3.9%
F 304
 
3.8%
Other values (14) 1797
22.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7906
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1557
19.7%
C 1032
13.1%
N 803
10.2%
L 537
 
6.8%
M 413
 
5.2%
Y 407
 
5.1%
T 394
 
5.0%
O 353
 
4.5%
I 309
 
3.9%
F 304
 
3.8%
Other values (14) 1797
22.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 7906
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1557
19.7%
C 1032
13.1%
N 803
10.2%
L 537
 
6.8%
M 413
 
5.2%
Y 407
 
5.1%
T 394
 
5.0%
O 353
 
4.5%
I 309
 
3.9%
F 304
 
3.8%
Other values (14) 1797
22.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1557
19.7%
C 1032
13.1%
N 803
10.2%
L 537
 
6.8%
M 413
 
5.2%
Y 407
 
5.1%
T 394
 
5.0%
O 353
 
4.5%
I 309
 
3.9%
F 304
 
3.8%
Other values (14) 1797
22.7%

DTI
Real number (ℝ)

Distinct1961
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.428287
Minimum0
Maximum29.85
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-02-04T22:47:57.197848image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.932
Q19.58
median14.45
Q319.47
95-th percentile24.214
Maximum29.85
Range29.85
Interquartile range (IQR)9.89

Descriptive statistics

Standard deviation6.3784458
Coefficient of variation (CV)0.4420792
Kurtosis-0.77034208
Mean14.428287
Median Absolute Deviation (MAD)4.94
Skewness-0.049035658
Sum57035.02
Variance40.68457
MonotonicityNot monotonic
2023-02-04T22:47:57.347483image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.8 9
 
0.2%
18.63 8
 
0.2%
20.88 8
 
0.2%
17.67 7
 
0.2%
16.4 7
 
0.2%
12.48 7
 
0.2%
9.65 7
 
0.2%
18.84 7
 
0.2%
19.63 7
 
0.2%
16.2 7
 
0.2%
Other values (1951) 3879
98.1%
ValueCountFrequency (%)
0 3
0.1%
0.02 2
0.1%
0.07 1
 
< 0.1%
0.2 1
 
< 0.1%
0.25 1
 
< 0.1%
0.32 2
0.1%
0.34 1
 
< 0.1%
0.41 1
 
< 0.1%
0.55 1
 
< 0.1%
0.57 1
 
< 0.1%
ValueCountFrequency (%)
29.85 1
< 0.1%
29.83 1
< 0.1%
29.73 1
< 0.1%
29.72 1
< 0.1%
29.63 1
< 0.1%
29.44 2
0.1%
29.36 1
< 0.1%
29.35 1
< 0.1%
29.29 1
< 0.1%
29.26 1
< 0.1%

Delinq 2Yrs
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10852517
Minimum0
Maximum6
Zeros3628
Zeros (%)91.8%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-02-04T22:47:57.475613image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.40879832
Coefficient of variation (CV)3.7668526
Kurtosis32.998701
Mean0.10852517
Median Absolute Deviation (MAD)0
Skewness4.9542972
Sum429
Variance0.16711607
MonotonicityNot monotonic
2023-02-04T22:47:57.573384image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 3628
91.8%
1 246
 
6.2%
2 61
 
1.5%
3 13
 
0.3%
4 4
 
0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 3628
91.8%
1 246
 
6.2%
2 61
 
1.5%
3 13
 
0.3%
4 4
 
0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
4 4
 
0.1%
3 13
 
0.3%
2 61
 
1.5%
1 246
 
6.2%
0 3628
91.8%

Inq Last 6Mths
Real number (ℝ)

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.85555274
Minimum0
Maximum8
Zeros1822
Zeros (%)46.1%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-02-04T22:47:57.685846image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.99702501
Coefficient of variation (CV)1.1653577
Kurtosis2.1636893
Mean0.85555274
Median Absolute Deviation (MAD)1
Skewness1.2652602
Sum3382
Variance0.99405886
MonotonicityNot monotonic
2023-02-04T22:47:57.797516image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1822
46.1%
1 1245
31.5%
2 584
 
14.8%
3 265
 
6.7%
4 21
 
0.5%
5 10
 
0.3%
6 3
 
0.1%
7 2
 
0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 1822
46.1%
1 1245
31.5%
2 584
 
14.8%
3 265
 
6.7%
4 21
 
0.5%
5 10
 
0.3%
6 3
 
0.1%
7 2
 
0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 2
 
0.1%
6 3
 
0.1%
5 10
 
0.3%
4 21
 
0.5%
3 265
 
6.7%
2 584
 
14.8%
1 1245
31.5%
0 1822
46.1%

Pub Rec
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
0
3831 
1
 
120
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3953
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3831
96.9%
1 120
 
3.0%
2 2
 
0.1%

Length

2023-02-04T22:47:57.922226image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-04T22:47:58.046750image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3831
96.9%
1 120
 
3.0%
2 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 3831
96.9%
1 120
 
3.0%
2 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3953
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3831
96.9%
1 120
 
3.0%
2 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3953
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3831
96.9%
1 120
 
3.0%
2 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3953
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3831
96.9%
1 120
 
3.0%
2 2
 
0.1%

Revol Bal
Real number (ℝ)

Distinct3672
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14367.448
Minimum0
Maximum140967
Zeros42
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-02-04T22:47:58.170421image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1240.4
Q16352
median11449
Q318151
95-th percentile35148.4
Maximum140967
Range140967
Interquartile range (IQR)11799

Descriptive statistics

Standard deviation13468.635
Coefficient of variation (CV)0.93744101
Kurtosis18.01765
Mean14367.448
Median Absolute Deviation (MAD)5657
Skewness3.3220358
Sum56794520
Variance1.8140412 × 108
MonotonicityNot monotonic
2023-02-04T22:47:58.333526image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 42
 
1.1%
6565 3
 
0.1%
18467 3
 
0.1%
10078 3
 
0.1%
10980 3
 
0.1%
11338 3
 
0.1%
8032 3
 
0.1%
15183 3
 
0.1%
13034 3
 
0.1%
6314 3
 
0.1%
Other values (3662) 3884
98.3%
ValueCountFrequency (%)
0 42
1.1%
3 1
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
16 1
 
< 0.1%
25 1
 
< 0.1%
33 1
 
< 0.1%
41 2
 
0.1%
50 1
 
< 0.1%
62 1
 
< 0.1%
ValueCountFrequency (%)
140967 1
< 0.1%
131949 1
< 0.1%
130920 1
< 0.1%
124744 1
< 0.1%
123416 1
< 0.1%
120504 1
< 0.1%
112522 1
< 0.1%
110856 1
< 0.1%
108339 1
< 0.1%
106406 1
< 0.1%

Total Paymnt
Real number (ℝ)

Distinct3710
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14435.064
Minimum0
Maximum58886.473
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-02-04T22:47:58.506862image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2401.064
Q16614.7872
median11907.35
Q319190.68
95-th percentile35788.924
Maximum58886.473
Range58886.473
Interquartile range (IQR)12575.893

Descriptive statistics

Standard deviation10492.53
Coefficient of variation (CV)0.72687798
Kurtosis1.5938309
Mean14435.064
Median Absolute Deviation (MAD)5937.1769
Skewness1.261679
Sum57061809
Variance1.1009319 × 108
MonotonicityNot monotonic
2023-02-04T22:47:58.657525image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14288.76169 8
 
0.2%
11907.34732 7
 
0.2%
12029.45 7
 
0.2%
13148.13786 7
 
0.2%
11600.98 6
 
0.2%
14288.77 5
 
0.1%
13263.96 5
 
0.1%
11726.32 5
 
0.1%
9011.557494 5
 
0.1%
10956.77596 5
 
0.1%
Other values (3700) 3893
98.5%
ValueCountFrequency (%)
0 2
0.1%
91.39 1
< 0.1%
151.8 1
< 0.1%
165.37 1
< 0.1%
203.55 1
< 0.1%
258.46 1
< 0.1%
262.7 1
< 0.1%
309.36 1
< 0.1%
328.01 1
< 0.1%
331.83 1
< 0.1%
ValueCountFrequency (%)
58886.47343 1
< 0.1%
58133.3199 1
< 0.1%
58090.95207 1
< 0.1%
58071.19982 1
< 0.1%
58071.19977 1
< 0.1%
57997.27995 1
< 0.1%
57143.25996 1
< 0.1%
57117.89995 1
< 0.1%
56681.8859 1
< 0.1%
56681.88585 1
< 0.1%

Interactions

2023-02-04T22:47:50.395253image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:37.388913image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:38.799412image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:40.207826image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:41.914977image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:43.348612image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:44.717280image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:46.101957image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:47.531481image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:48.934437image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:50.539361image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:37.526543image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:38.940039image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:40.351347image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:42.057595image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:43.485527image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:44.871866image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:46.245400image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:47.684774image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:49.076564image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:50.679357image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:37.667152image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:39.082512image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:40.493793image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:42.203206image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:43.624635image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:45.011070image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:46.404973image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:47.826396image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:49.240809image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:50.823009image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:37.810233image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:39.224671image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:40.635989image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:42.347819image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:43.764260image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:45.159200image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:46.547293image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:47.971513image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:49.422378image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:50.958496image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:37.949969image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:39.362852image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:40.775481image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:42.480087image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:43.903370image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:45.289851image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:46.682049image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:48.107480image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:49.557982image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:51.089262image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:38.096223image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:39.497070image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:41.197335image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:42.609959image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:44.036016image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:45.425951image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:46.807712image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:48.241386image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:49.692041image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:51.219962image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:38.238827image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:39.636971image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:41.332263image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:42.755086image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:44.167891image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:45.556758image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:46.937365image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:48.376671image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:49.832783image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:51.351755image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:38.374138image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:39.777126image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:41.477874image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:42.887012image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:44.295875image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:45.687525image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:47.067195image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:48.510949image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:49.969634image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:51.492608image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:38.518188image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:39.924583image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:41.624449image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:43.043646image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:44.438563image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:45.828656image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:47.244719image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:48.653738image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:50.117770image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:51.630362image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:38.659259image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:40.070194image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:41.773356image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:43.212074image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:44.579648image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:45.970309image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:47.397207image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:48.796610image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-04T22:47:50.257590image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2023-02-04T22:47:58.810488image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Loan AmntFunded amnt invInt RateINSTALLMENTAnnual IncDTIDelinq 2YrsInq Last 6MthsRevol BalTotal PaymntGenderTERMGRADESub GradeHome OwnershipVerification StatusLoan WriteoffPURPOSEAdd StatePub Rec
Loan Amnt1.0000.9950.3110.9600.4390.045-0.039-0.0010.4350.8750.0000.5170.1710.1660.1350.3650.0830.1200.0350.065
Funded amnt inv0.9951.0000.3020.9690.4370.044-0.0410.0040.4330.8780.0000.4930.1650.1600.1310.3550.0860.1200.0330.070
Int Rate0.3110.3021.0000.2740.1140.1140.1300.2140.1800.2570.0250.5850.9020.9960.0820.1850.2270.0520.0240.055
INSTALLMENT0.9600.9690.2741.0000.4390.037-0.0300.0130.4320.8490.0000.3100.1760.1720.1060.3210.0710.1110.0080.068
Annual Inc0.4390.4370.1140.4391.000-0.1800.0660.0240.3940.4090.0000.1320.0700.0770.1970.1510.0530.0710.0000.000
DTI0.0450.0440.1140.037-0.1801.000-0.0520.0390.2890.0320.0000.0700.0740.0810.0510.1300.0720.0860.0100.021
Delinq 2Yrs-0.039-0.0410.130-0.0300.066-0.0521.000-0.024-0.102-0.0350.0370.0250.0690.1020.0220.0280.0420.0360.0230.000
Inq Last 6Mths-0.0010.0040.2140.0130.0240.039-0.0241.0000.002-0.0170.0000.0600.1150.1260.0610.0000.0660.0660.0880.000
Revol Bal0.4350.4330.1800.4320.3940.289-0.1020.0021.0000.3990.0380.1780.0620.0570.1490.1700.0130.0530.0000.054
Total Paymnt0.8750.8780.2570.8490.4090.032-0.035-0.0170.3991.0000.0420.4470.1730.1850.1130.3240.2870.1020.0300.112
Gender0.0000.0000.0250.0000.0000.0000.0370.0000.0380.0421.0000.0000.0080.0000.0120.0170.0000.0000.0350.000
TERM0.5170.4930.5850.3100.1320.0700.0250.0600.1780.4470.0001.0000.5610.5980.1200.3150.1880.1680.0720.000
GRADE0.1710.1650.9020.1760.0700.0740.0690.1150.0620.1730.0080.5611.0000.9960.0740.1810.2210.0510.0150.053
Sub Grade0.1660.1600.9960.1720.0770.0810.1020.1260.0570.1850.0000.5980.9961.0000.0940.1870.2390.0420.0000.070
Home Ownership0.1350.1310.0820.1060.1970.0510.0220.0610.1490.1130.0120.1200.0740.0941.0000.0890.0410.1570.1950.000
Verification Status0.3650.3550.1850.3210.1510.1300.0280.0000.1700.3240.0170.3150.1810.1870.0891.0000.0340.1120.0520.000
Loan Writeoff0.0830.0860.2270.0710.0530.0720.0420.0660.0130.2870.0000.1880.2210.2390.0410.0341.0000.0810.0000.013
PURPOSE0.1200.1200.0520.1110.0710.0860.0360.0660.0530.1020.0000.1680.0510.0420.1570.1120.0811.0000.0140.027
Add State0.0350.0330.0240.0080.0000.0100.0230.0880.0000.0300.0350.0720.0150.0000.1950.0520.0000.0141.0000.072
Pub Rec0.0650.0700.0550.0680.0000.0210.0000.0000.0540.1120.0000.0000.0530.0700.0000.0000.0130.0270.0721.000

Missing values

2023-02-04T22:47:51.866146image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-04T22:47:52.315889image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-04T22:47:52.591941image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NameEmail IDGenderDt_AppliedUniversityLoan AmntFunded amnt invTERMInt RateINSTALLMENTGRADESub GradeHome OwnershipAnnual IncVerification StatusLoan WriteoffPURPOSEZip CodeAdd StateDTIDelinq 2YrsInq Last 6MthsPub RecRevol BalTotal Paymnt
0Calley Gironcgiron0@ehow.comFemale01/01/81Warner Southern College50004975.036 months0.107162.87BB2RENT24000.0Verified0credit_card860xxAZ27.65010136485863.155187
1Linus Studlstud1@washington.eduMale02/01/81Shri Lal Bahadur Shastri Rashtriya Sanskrit Vidyapeetha25002500.060 months0.15359.83CC4RENT30000.0Source Verified1car309xxGA1.0005016871014.530000
2Lorelle Ambagelambage2@wix.comFemale03/01/81Technische Universität Bergakademie Freiberg24002400.036 months0.16084.33CC5RENT12252.0Not Verified0small_business606xxIL8.7202029563005.666844
3Anna-diane Larratalarrat3@economist.comFemale04/01/81Divine Word College of Legazpi1000010000.036 months0.135339.31CC1RENT49200.0Source Verified0other917xxCA20.00010559812231.890000
4Gill RuskeNaNFemale05/01/81East China Jiao Tong University30003000.060 months0.12767.79BB5RENT80000.0Source Verified0other972xxOR17.94000277834066.908161
5Evelyn MacFaulemacfaul5@theatlantic.comFemale06/01/81Ahmedabad University50005000.036 months0.079156.46AA4RENT36000.0Source Verified0wedding852xxAZ11.2003079635632.210000
6Ainslie Rainardarainard6@virginia.eduFemale07/01/81NaN70007000.060 months0.160170.08CC5RENT47004.0Not Verified0debt_consolidation280xxNC23.510101772610137.840010
7Emmott Hambyehamby7@prnewswire.comMale08/01/81Institute of Business Management30003000.036 months0.186109.43EE1RENT48000.0Source Verified0car900xxCA5.3502082213939.135294
8Shem Toomerstoomer8@home.plMale09/01/81Osaka University of Education56005600.060 months0.213152.39FF2OWN40000.0Source Verified1small_business958xxCA5.550205210647.500000
9Giana Aberhartgaberhart9@mozilla.comFemale10/01/81American Public University53755350.060 months0.127121.45BB5RENT15000.0Verified1other774xxTX18.0800092791484.590000
NameEmail IDGenderDt_AppliedUniversityLoan AmntFunded amnt invTERMInt RateINSTALLMENTGRADESub GradeHome OwnershipAnnual IncVerification StatusLoan WriteoffPURPOSEZip CodeAdd StateDTIDelinq 2YrsInq Last 6MthsPub RecRevol BalTotal Paymnt
3943Merla Thebemthebeq7@cocolog-nifty.comFemale21/10/91North Eastern Hill University60006000.036 months0.163211.81DD1RENT39564.0Verified1debt_consolidation606xxIL23.7821020283388.960000
3944Marcellina Dinnegesmdinnegesq8@infoseek.co.jpFemale22/10/91Universidade Católica de Santos24002400.036 months0.11779.39BB3RENT39800.0Not Verified0other303xxGA14.32000154972836.660516
3945Way Symondswsymondsq9@mlb.comMale23/10/91American International University West Africa2500025000.060 months0.183638.25DD5MORTGAGE156000.0Source Verified0house944xxCA5.850001070937936.750000
3946Ailene MatejkaNaNFemale24/10/91Kaya University2000020000.036 months0.117661.52BB3RENT80700.0Verified0debt_consolidation946xxCA13.67010721123406.523000
3947Samuel OverelNaNMale25/10/91Northwestern University1200012000.060 months0.183306.36DD5MORTGAGE34000.0Not Verified1debt_consolidation177xxPA12.5600061149667.950000
3948Corbie Creeboeccreeboeqc@sitemeter.comMale26/10/91Shaheed Rajaei Teacher Training University1200012000.036 months0.135407.17CC1RENT125000.0Source Verified0wedding086xxNJ13.180104628614657.917650
3949Bobbe Ochterloniebochterlonieqd@ezinearticles.comFemale27/10/91Dhofar University1500015000.036 months0.124501.23BB4RENT72000.0Verified0debt_consolidation104xxNY7.470101214716729.253640
3950Corella Espositocespositoqe@macromedia.comFemale28/10/91University of Jan Evangelista Purkyne1200012000.036 months0.060365.23AA1OWN48000.0Not Verified0debt_consolidation365xxAL23.350002238513148.137860
3951Prince Dibdinpdibdinqf@businessinsider.comMale29/10/91College in Sládkovičovo1500015000.060 months0.160364.46CC5RENT50000.0Verified1debt_consolidation907xxCA18.26010979910883.540000
3952Georgette Warrattgwarrattqg@java.comFemale30/10/91Technical University of Lublin1500014975.060 months0.153358.98CC4MORTGAGE32976.0Not Verified1debt_consolidation177xxPA17.90010795611704.260000